Prototype Learning of Inter-network Connectivity for ASD Diagnosis and Personalized Analysis

Eunsong Kang, Da Woon Heo, Heung Il Suk

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In recent studies, deep learning has shown great potential to explore topological properties of functional connectivity (FC), e.g., graph neural networks (GNNs), for brain disease diagnosis, e.g., Autism spectrum disorder (ASD). However, many of the existing methods integrate the information locally, e.g., among neighboring nodes in a graph, which hinders from learning complex patterns of FC globally. In addition, their analysis for discovering imaging biomarkers is confined to providing the most discriminating regions without considering individual variations over the average FC patterns of groups, i.e., patients and normal controls. To address these issues, we propose a unified framework that globally captures properties of inter-network connectivity for classification and provides individual-specific group characteristics for interpretation via prototype learning. In our experiments using the ABIDE dataset, we validated the effectiveness of the proposed framework by comparing with competing topological deep learning methods in the literature. Furthermore, we individually analyzed functional mechanisms of ASD for neurological interpretation.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings
EditorsLinwei Wang, Qi Dou, P. Thomas Fletcher, Stefanie Speidel, Shuo Li
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages10
ISBN (Print)9783031164361
Publication statusPublished - 2022
Event25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022 - Singapore, Singapore
Duration: 2022 Sep 182022 Sep 22

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13433 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022


  • Autism spectrum disorder
  • Inter-network connectivity
  • Prototype learning
  • Resting-State functional magnetic resonance imaging
  • Transformer

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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